CN107146200A - A kind of unmanned aerial vehicle remote sensing image split-joint method based on image mosaic quality evaluation - Google Patents
A kind of unmanned aerial vehicle remote sensing image split-joint method based on image mosaic quality evaluation Download PDFInfo
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Abstract
The present invention relates to technical field of image processing, more specifically, it is related to a kind of unmanned aerial vehicle remote sensing image split-joint method based on image mosaic quality evaluation, the latitude and longitude information and elevation information of unmanned plane are recorded when gathering each secondary remote sensing images, the shake for removing remote sensing images using Key dithering fuzzy algorithmic approach is obscured, and the remote sensing images that splicing requirement is unsatisfactory in remote sensing images sequence are found out using image mosaic quality evaluation, unmanned plane is gathered again according to the latitude and longitude information and elevation information of record to the remote sensing images for being unsatisfactory for requiring, and the new remote sensing images collected again are put into original remote sensing images sequence replace and supplement corresponding original remote sensing images, image registration and splicing are carried out again, avoid the repeated acquisition to effective image and splicing, the splicing efficiency of image is improved while reducing workload.
Description
Technical field
The present invention relates to technical field of image processing, more particularly, to a kind of nothing based on image mosaic quality evaluation
Man-machine remote sensing images joining method.
Background technology
Image mosaic is exactly the mistake that the image that several have lap is combined into seamless high-definition picture one large-scale
Journey, and these images are probably what different time, different visual angles or different sensors were obtained.Image mosaic technology is mainly used in
The fields such as military affairs, remote sensing, mapping, medical science and computer vision.With the fast development of unmanned air vehicle technique, unmanned plane is with high-resolution
Rate, high flexibility, high efficiency and inexpensive advantage are widely used in natural calamity regional assessment, resource exploration, remote sensing survey
Paint and environmental protection in terms of, therefore the registration of unmanned aerial vehicle remote sensing image receives extensive attention with splicing, many countries and
Unit has all carried out the research to unmanned plane image mosaic correlation technique.
Present unmanned aerial vehicle remote sensing image mosaic process be first progress flight path planning, allow unmanned plane along planning fly
Row track gathers image, and then the image sequence of collection is spliced again.It is merged when having not being inconsistent in the image sequence collected
Requirement or image sequence are connect when planning region is not completely covered, then using unmanned plane along with the flight path of preplanning again
Secondary collection image, then the image gathered twice is merged and is spliced, this causes workload big, and image is largely repeated, splicing
Efficiency is low.
The content of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of nobody based on image mosaic quality evaluation
Machine remote sensing images joining method, is avoided that the repeated acquisition to effective image and splicing, and image is improved while reducing workload
Splicing efficiency.
Effective image is that can effectively apply the image in splicing.
To reach above-mentioned purpose, the technical solution adopted by the present invention is：
A kind of unmanned aerial vehicle remote sensing image split-joint method based on image mosaic quality evaluation is provided, it is characterised in that including
Following steps：
S1. it is consistent by the system time setting of video camera on the system time and unmanned plane of unmanned plane, in whole remote sensing
During IMAQ, by the longitude and latitude of unmanned plane when taking pictures each time and altitude record in the Air Diary of unmanned plane；
S2. registration and splicing are carried out to the original remote sensing images sequence collected；
S3. the splicing result to every two original remote sensing images carries out quality evaluation, if quality evaluation is less than threshold value H,
Key dithering Fuzzy Processing then is carried out to two original remote sensing images, is then spliced again, spliced result is carried out again
Quality evaluation, if quality evaluation is also less than threshold value H, the temporal information of two original remote sensing images is recorded, after
The continuous registration for carrying out original remote sensing images sequence and splicing；
S4. after the completion of original remote sensing images sequence assembly, check whether spliced remote sensing images cover all planning
Region, it is in spliced remote sensing images that white space and its neighbouring part is distant if there is unlapped white space
Sense image interception comes out, and does matching operation with original remote sensing images sequence with the remote sensing images of interception out, finds out matching rate most
The original remote sensing images of high several and the temporal information for recording these remote sensing images；
S5. according to the temporal information of the original remote sensing images recorded in S3 and S4, found in the Air Diary of unmanned plane
The longitude and latitude and elevation information during these remote sensing images are shot, the flight of unmanned plane is planned again according to longitude and latitude and elevation information
These remote sensing images are gathered by track again；
S6. the new remote sensing images collected again are put into original remote sensing images sequence and replace and supplement corresponding original
Beginning remote sensing images, carry out image registration and splicing again；
S7. S2 to S6 is repeated, the splicing remote sensing images until obtaining meeting demand.
A kind of unmanned aerial vehicle remote sensing image split-joint method based on image mosaic quality evaluation of the present invention, gathering, each pair is distant
The latitude and longitude information and elevation information of unmanned plane are recorded during sense image, remote sensing images are removed using Key dithering fuzzy algorithmic approach
Shake obscure, and find out using image mosaic quality evaluation the remote sensing figure that splicing requirement is unsatisfactory in remote sensing images sequence
Picture, unmanned plane is gathered again according to the latitude and longitude information and elevation information of record to the remote sensing images for being unsatisfactory for requiring, and
The new remote sensing images collected again are put into original remote sensing images sequence and replace and supplement corresponding original remote sensing images,
Image registration and splicing are carried out again, it is to avoid repeated acquisition and splicing to effective image, improved while reducing workload
The splicing efficiency of image.
Preferably, quality is carried out to the splicing result of every two original remote sensing images using edge Difference Spectrum evaluation assessment in S3
Evaluate.The English full name of edge Difference Spectrum evaluation assessment is difference of edge map, is abbreviated as DoEM,.
Preferably, debounce dynamic model is carried out to two original remote sensing images using the Wiener Filtering with optimal window in S3
Paste processing.
Compared with prior art, the beneficial effects of the invention are as follows：
A kind of unmanned aerial vehicle remote sensing image split-joint method based on image mosaic quality evaluation of the present invention, gathering, each pair is distant
The latitude and longitude information and elevation information of unmanned plane are recorded during sense image, remote sensing images are removed using Key dithering fuzzy algorithmic approach
Shake obscure, and find out using image mosaic quality evaluation the remote sensing figure that splicing requirement is unsatisfactory in remote sensing images sequence
Picture, unmanned plane is gathered again according to the latitude and longitude information and elevation information of record to the remote sensing images for being unsatisfactory for requiring, and
The new remote sensing images collected again are put into original remote sensing images sequence and replace and supplement corresponding original remote sensing images,
Image registration and splicing are carried out again, it is to avoid repeated acquisition and splicing to effective image, improved while reducing workload
The splicing efficiency of image.
Brief description of the drawings
Fig. 1 is a kind of flow of the unmanned aerial vehicle remote sensing image split-joint method based on image mosaic quality evaluation of the present embodiment
Figure.
Embodiment
With reference to embodiment, the present invention is further illustrated.Wherein, being given for example only property of accompanying drawing illustrates,
What is represented is only schematic diagram, rather than pictorial diagram, it is impossible to be interpreted as the limitation to this patent；In order to which the reality of the present invention is better described
Example is applied, some parts of accompanying drawing have omission, zoomed in or out, and do not represent the size of actual product；To those skilled in the art
For, some known features and its explanation may be omitted and will be understood by accompanying drawing.
The same or analogous part of same or analogous label correspondence in the accompanying drawing of the embodiment of the present invention；In retouching for the present invention
In stating, it is to be understood that if the orientation or position relationship that have the instructions such as term " on ", " under ", "left", "right" are based on accompanying drawing
Shown orientation or position relationship, are for only for ease of the description present invention and simplify description, rather than indicate or imply meaning
Device or element must have specific orientation, with specific azimuth configuration and operation, therefore position relationship described in accompanying drawing
Term being given for example only property explanation, it is impossible to be interpreted as the limitation to this patent, for the ordinary skill in the art, can
To understand the concrete meaning of above-mentioned term as the case may be.
Embodiment
A kind of flow chart such as Fig. 1 of the unmanned aerial vehicle remote sensing image split-joint method based on image mosaic quality evaluation of the present embodiment
It is shown, comprise the following steps：
S1. it is consistent by the system time setting of video camera on the system time and unmanned plane of unmanned plane, in whole remote sensing
During IMAQ, by the longitude and latitude of unmanned plane when taking pictures each time and altitude record in the Air Diary of unmanned plane；
Specifically, according to the parameter of Airborne Camera, the angle of camera lens, the flying speed of unmanned plane and flying height, treat
Splicing regions carry out the whole Air Diary for recording unmanned plane, bag during flight path planning, collection remote sensing images sequence
Longitude and latitude and height when including flight；
S2. registration and splicing are carried out to the original remote sensing images sequence collected；
Specifically, process of image registration detects characteristic point using SIFT algorithms, and Mismatching point is rejected using RANSAC algorithms,
Using the optimal stitching line searching algorithm cut based on figure to eliminate the influence of parallax in image mosaic；
S3. the splicing result to every two original remote sensing images carries out quality evaluation, if quality evaluation is less than threshold value H,
Key dithering Fuzzy Processing then is carried out to two original remote sensing images, is then spliced again, spliced result is carried out again
Quality evaluation, if quality evaluation is also less than threshold value H, the temporal information of two original remote sensing images is recorded, after
The continuous registration for carrying out original remote sensing images sequence and splicing；
In the present embodiment, using edge Difference Spectrum evaluation assessment (DoEM, difference of edge map) in S2
The splicing results of every two original remote sensing images carries out quality evaluation, using the splicing result of every two original remote sensing images as treating
Remote sensing images are evaluated, its algorithm is as follows：
Step 1: Image Edge-Detection, the original remote sensing images conversion first by remote sensing images to be evaluated and without splicing
For gray level image, edge extracting is then carried out respectively using Sobel edge detection operators, edge image is obtained；
Step 2: building the edge Difference Spectrum of image, calculus of differences is carried out to edge image respectively, edge image pair is obtained
The edge Difference Spectrum answered；
Step 3: counting edge Difference Spectrum information and calculating scoring, scoring formula is as follows
Wherein, μeFor edge Difference Spectrum transitional region frontier district average, μaFor the overall average of transitional region, σ2For transition region
Domain entirety variance.C1、C2、C3、C4Respectively 4 constants, wherein, C1、C2According to phase of the scoring with edge Difference Spectrum Change in Mean
Pass degree is determined；C3、C4Place is class normal distribution curve, is chosen according to 3 σ criterions and corrects determination；In the present embodiment, warp
Cross and C is used after many experiments1=80, C2=50, C3=600, C4=256；
In the present embodiment, the result of DOEM quality evaluations is between 0~1, and threshold value H uses 0.7；
If the result of quality evaluation is less than threshold value H, using the Wiener Filtering with optimal window to original remote sensing
Image carries out Key dithering Fuzzy Processing；
Original remote sensing images after deblurring is handled are spliced again, matter is carried out again to spliced result
Amount is evaluated, if quality evaluation is also less than threshold value H, the temporal information of two original remote sensing images is recorded, and is continued
Carry out registration and the splicing of original remote sensing images sequence；
S4. after the completion of original remote sensing images sequence assembly, check whether spliced remote sensing images cover all planning
Region, it is in spliced remote sensing images that white space and its neighbouring part is distant if there is unlapped white space
Sense image interception comes out, and does matching operation with original remote sensing images sequence with the remote sensing images of interception out, finds out matching rate most
The original remote sensing images of high several and the temporal information for recording these remote sensing images；
S5. according to the temporal information of the original remote sensing images recorded in S3 and S4, found in the Air Diary of unmanned plane
The longitude and latitude and elevation information during these remote sensing images are shot, the flight of unmanned plane is planned again according to longitude and latitude and elevation information
These remote sensing images are gathered by track again；
S6. the new remote sensing images collected again are put into original remote sensing images sequence and replace and supplement corresponding original
Beginning remote sensing images, carry out image registration and splicing again；
S7. S2 to S6 is repeated, the splicing remote sensing images until obtaining meeting demand.
A kind of unmanned aerial vehicle remote sensing image split-joint method based on image mosaic quality evaluation of the present invention, gathering, each pair is distant
The latitude and longitude information and elevation information of unmanned plane are recorded during sense image, remote sensing images are removed using Key dithering fuzzy algorithmic approach
Shake obscure, and find out using image mosaic quality evaluation the remote sensing figure that splicing requirement is unsatisfactory in remote sensing images sequence
Picture, unmanned plane is gathered again according to the latitude and longitude information and elevation information of record to the remote sensing images for being unsatisfactory for requiring, and
The new remote sensing images collected again are put into original remote sensing images sequence and replace and supplement corresponding original remote sensing images,
Image registration and splicing are carried out again, it is to avoid repeated acquisition and splicing to effective image, improved while reducing workload
The splicing efficiency of image.
Obviously, the above embodiment of the present invention is only intended to clearly illustrate example of the present invention, and is not pair
The restriction of embodiments of the present invention.For those of ordinary skill in the field, may be used also on the basis of the above description
To make other changes in different forms.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Any modifications, equivalent substitutions and improvements made within the spirit and principle of invention etc., should be included in the claims in the present invention
Protection domain within.
Claims (3)
1. a kind of unmanned aerial vehicle remote sensing image split-joint method based on image mosaic quality evaluation, it is characterised in that including following step
Suddenly：
S1. it is consistent by the system time setting of video camera on the system time and unmanned plane of unmanned plane, in whole remote sensing images
During collection, by the longitude and latitude of unmanned plane when taking pictures each time and altitude record in the Air Diary of unmanned plane；
S2. registration and splicing are carried out to the original remote sensing images sequence collected；
S3. the splicing result to every two original remote sensing images carries out quality evaluation, right if quality evaluation is less than threshold value H
Two original remote sensing images carry out Key dithering Fuzzy Processing, then spliced again, quality is carried out again to spliced result
Evaluate, if quality evaluation is also less than threshold value H, the temporal information of two original remote sensing images is recorded, continue into
The registration of the original remote sensing images sequence of row and splicing；
S4. after the completion of original remote sensing images sequence assembly, check whether spliced remote sensing images cover all planning regions,
If there is unlapped white space, by white space and its neighbouring part remote sensing images in spliced remote sensing images
Interception comes out, and does matching operation with original remote sensing images sequence with the remote sensing images of interception out, finds out matching rate highest several
Original remote sensing images and the temporal information for recording these remote sensing images；
S5. according to the temporal information of the original remote sensing images recorded in S3 and S4, shooting is found in the Air Diary of unmanned plane
Longitude and latitude and elevation information during these remote sensing images, the flight rail of unmanned plane is planned according to longitude and latitude and elevation information again
These remote sensing images are gathered by mark again；
S6. the new remote sensing images collected again are put into original remote sensing images sequence and replace and supplement corresponding original distant
Feel image, image registration and splicing are carried out again；
S7. S2 to S6 is repeated, the splicing remote sensing images until obtaining meeting demand.
2. a kind of unmanned aerial vehicle remote sensing image split-joint method based on image mosaic quality evaluation according to claim 1, its
It is characterised by, quality evaluation is carried out to the splicing result of every two original remote sensing images using edge Difference Spectrum evaluation assessment in S3.
3. a kind of unmanned aerial vehicle remote sensing image split-joint method based on image mosaic quality evaluation according to claim 1, its
It is characterised by, the fuzzy place of Key dithering is carried out to two original remote sensing images using the Wiener Filtering with optimal window in S3
Reason.
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| CN111141264A (en) * | 2019-12-31 | 2020-05-12 | 中国电子科技集团公司信息科学研究院 | Unmanned aerial vehicle-based urban three-dimensional mapping method and system |
| CN111369481A (en) * | 2020-02-28 | 2020-07-03 | 当家移动绿色互联网技术集团有限公司 | Image fusion method and device, storage medium and electronic equipment |
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| CN117911294A (en) * | 2024-03-18 | 2024-04-19 | 浙江托普云农科技股份有限公司 | Corn ear surface image correction method, system and device based on vision |
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| CN109064456A (en) * | 2018-07-19 | 2018-12-21 | 西安工业大学 | The seam significance degree detection method of digital camouflage splicing |
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| CN110381259A (en) * | 2019-08-13 | 2019-10-25 | 广州欧科信息技术股份有限公司 | Mural painting image collecting method, device, computer equipment and storage medium |
| CN110381259B (en) * | 2019-08-13 | 2021-08-31 | 广州欧科信息技术股份有限公司 | Mural image acquisition method and device, computer equipment and storage medium |
| WO2021037257A1 (en) * | 2019-08-30 | 2021-03-04 | 中兴通讯股份有限公司 | Scene reconstruction system and method |
| CN111141264A (en) * | 2019-12-31 | 2020-05-12 | 中国电子科技集团公司信息科学研究院 | Unmanned aerial vehicle-based urban three-dimensional mapping method and system |
| CN111141264B (en) * | 2019-12-31 | 2022-06-28 | 中国电子科技集团公司信息科学研究院 | Unmanned aerial vehicle-based urban three-dimensional mapping method and system |
| CN111369481A (en) * | 2020-02-28 | 2020-07-03 | 当家移动绿色互联网技术集团有限公司 | Image fusion method and device, storage medium and electronic equipment |
| CN112990017B (en) * | 2021-03-16 | 2022-01-28 | 刘宏伟 | Smart city big data analysis method and monitoring system |
| CN112990017A (en) * | 2021-03-16 | 2021-06-18 | 陈永欢 | Smart city big data analysis method and monitoring system |
| CN115657706A (en) * | 2022-09-22 | 2023-01-31 | 中铁八局集团第一工程有限公司 | Landform measuring method and system based on unmanned aerial vehicle |
| CN116228539A (en) * | 2023-03-10 | 2023-06-06 | 贵州师范大学 | Unmanned aerial vehicle remote sensing image stitching method |
| CN117911294A (en) * | 2024-03-18 | 2024-04-19 | 浙江托普云农科技股份有限公司 | Corn ear surface image correction method, system and device based on vision |
| CN117911294B (en) * | 2024-03-18 | 2024-05-31 | 浙江托普云农科技股份有限公司 | Corn ear surface image correction method, system and device based on vision |
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